Slope displacement prediction using hybird soft computing algorithms

Rainfall-induced landslide risk has been increasing all over the world during the last decades. This paper proposes a wireless sensing node network (WSNN) system for landslide monitoring and early warning. A full-scale WSNN system has been implemented on a slope based on on-site survey in a village area, Southeast China. The system consists of soil moisture sensors, inclinometers, piezometers, and rain gauge that were installed on the slope. Given the nonstationary and complex characteristic of the slope deformation, this paper proposes a slope displacement prediction model and an early warming framework based on a set of sequential intelligent computing algorithms that can take advantages of Rough Set theory (RS), Kernel principal component analysis (KPCA), quantum particle swarm optimization (QPSO), least square support vector machine (LSSVM), and Markov chain (MC). The results demonstrate that the proposed approach achieves higher prediction accuracy, faster convergence, and better generalization compared with existing prevalent models.